个人简介
孟军，工学博士，大连理工大学计算机学院，教授，博士生导师。主要从事数据挖掘和机器学习算法及其在生物信息领域的应用方面的研究。主持和参与了国家自然科学基金、国家重大专项、教育部专项和省级自然基金等项目十余项，其中主持的辽宁省自然科学基金项目《相容粒计算分类知识发现及其应用研究》已完成，主持的国家自然科学基金面上项目《基于植物胁迫响应基因表达数据和GO术语结合的特征选择及调控网络研究》已完成, 主持的《基于集成深度学习的植物lncRNA与miRNA互作关系预测研究》正在进行中。研究成果发表在Briefings in Bioinformatics(IF 13.994), Bioinformatics (IF 6.931), Applied Soft Computing（IF 8.263），Neurocomputing（IF 5.779）,Cells（IF 7.666），IEEE/ACM Transactions on Computational Biology and Bioinformatics（IF 3.702）等国际期刊和计算机研究与发展等国内核心期刊上。曾多次获辽宁省和大连市自然科学学术成果二等奖。已培养硕士研究生40余人(包括留学生2人)，其中，4人获校优秀研究生（其中，2人为博士生），4人获优秀硕士学位论文。
近期论文
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1. Kang Qiang, Meng Jun*, Luan Yushi*. RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction. Briefings in bioinformatics,2022, 23(3): 1-10.
2. Kang Qiang, Meng Jun* ,Su Chenglin, Luan Yushi*.Mining plant endogenous target mimics from miRNA–lncRNA interactions based on dual-path parallel ensemble pruning method. Briefings in Bioinformatics,2021,23(1):1-11.
3. Zhao Siyuan, Meng Jun*, Kang Qiang, Luan Yushi*. Identifying lncRNA-encoded Short Peptides Using Optimized Hybrid Features and Ensemble Learning. IEEE-Acm Transactions on Computational Biology and Bioinformatics, 2021, Aug 12. https://ieeexplore.ieee.org/document/9512408.
4. Zhao Siyuan, Meng Jun*, Luan Yushi. LncRNA-Encoded Short Peptides Identification Using Feature Subset Recombination and Ensemble Learning. Interdisciplinary Sciences: Computational Life Sciences,2022,14(1):101-112..
5.Kang Qiang, Meng Jun*, Shi Wenhao, Luan Yushi. Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA-lncRNA Interaction Prediction. Interdisciplinary Sciences: Computational Life Sciences. 2021, 13: 603-614.
6. Kang Qiang, Meng Jun*, Cui Jun, Luan Yushi*, Chen Ming. PmliPred: a method based on hybrid model and fuzzy decision for plant miRNA-lncRNA interaction prediction. Bioinformatics, 2020, 36(10): 2986-2992.
7. Meng Jun, Kang Qiang, Chang Zheng, Luan Yushi*. PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles. BMC Bioinformatics. 2021, 22: 242.
8.Zhou Haoran, Wekesa Sanyanda Jael ,Luan Yushi,Meng Jun*.PRPI SC: an ensemble deep learning model for predicting plant lncRNA protein interactions. BMC Bioinformatics,2021,22:415
9. Wekesa Jael Sanyanda, Meng Jun*, Luan Yushi. Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction. Genomics, 2020, 112(5): 2028-2036.
10.Wekesa Jael Sanyanda, Meng Jun*, Luan Yushi. A deep learning model for plant lncRNA-protein interaction prediction with graph attention. Molecular Genetics and Genomics, 2020 May 15. doi: 10.1007/s00438-020-01682-w.
11. Zhang Peng, Meng Jun*, Luan Yushi, Liu Chanjuan. Plant miRNA-lncRNA Interaction Prediction Prediction with the Ensemble of CNN and IndRNN. Interdisciplinary Sciences: Computational Life Sciences, 2020, 12: 82–89.
12. Wekesa Jael Sanyanda, Luan Yushi, Chen Ming, Meng Jun*. A Hybrid Prediction Method for Plant lncRNA-Protein Interaction. Cells, 2019, 8(6): 521.
13. Ismalia Bouba, Kang Qiang, Luan Yushi, Meng Jun*. Predicting miRNA-lncRNA interactions and recognizing their regulatory roles in stress response of plants. Mathematical Biosciences, 2019, 312(6): 67-76.
14. Meng Jun, Chang Zheng, Zhang Peng, Shi Wenhao, Luan Yushi*. lncRNA-LSTM: Prediction of Plant Long Non-coding RNAs Using Long Short-Term Memory Based on p-nts Encoding. International Conference on Intelligent Computing, 2019, 11645:347-357.
15. Zhou Haoran, Luan Yushi, Wekesa Jael Sanyanda, Meng Jun*. Prediction of Plant lncRNA-Protein Interactions Using Sequence Information Based on Deep Learning. International Conference on Intelligent Computing, 2019, 11645: 358-368.
16.Meng Jun, Shi Guanli, Luan Yushi. Plant miRNA function prediction based on functional similarity network and transductive multi-label classification algorithm, Neurocomputing, 2016, 179:283-289.
17.Meng Jun, Zhang Jing, Li Rui, Luan Yushi. Gene selection using rough set based on neighborhood for the analysis of plant stress response, Applied Soft Computing, 2014, 25(1): 51-63.
18.Meng Jun, Liu Dong, Luan Yushi. Inferring plant microRNA functional similarity using a weighted protein-protein interaction network, BMC Bioinformatics, 2015, 16:360.
19. Meng Jun, Zhang Jing, Luan Yushi. Gene Selection Integrated with Biological Knowledge for Plant Stress Response Using Neighborhood System and Rough Set Theory, IEEE-ACM Transactions on Computational Biology and Bioinformatics, 2015, 12(2):433-444.
20.Meng Jun, Hao Han, Luan Yushi. Classifier ensemble selection based on affinity propagation clustering, Journal of Biomedical Informatics, 2016, 60:234-242.
21.Meng Jun, Zhang Jing, Luan Yush, He Xinyu, Li Lishuang, Zhu Yuanfeng. Parallel gene selection and dynamic ensemble pruning based on Affinity Propagation, Computers in Biology and Medicine, 2017, 87:8-21.
22 Meng Jun, Jiang Dingling, Zhang Jing, Luan Yushi. Ensemble classification for gene expression data based on parallel clustering, International Journal of Data Mining and Bioinformatics, 2018, 20(3):213-229.
23. Meng Jun, Wekesa Jaelsanyanda, Shi Guanli, Luan Yushi. Protein function prediction based on data fusion and functional interrelationship, Mathematical Biosciences, 2016, 274：25-32.
24.Meng Jun, Liu Dong, Sun Chao, Luan Yushi. Prediction of plant pre-microRNAs and their microRNAs in genome-scale sequences using structure-sequence features and support vector machine, BMC Bioinformatics, 2014, 15:423.
25.Meng Jun, Li Rui, Luan Yushi. Classification by integrating plant stress response gene expression data with biological knowledge, Mathematical Biosciences, 2015, 266:65-72.
26.Meng Jun, Zhang Xin, Luan Yushi. Global Propagation Method for Predicting Protein Function by Integrating Multiple Data Sources, Current Bioinformatics,2016,11(2) :186-194.
27.石文浩, 孟军*, 张朋, 刘婵娟. 融合CNN和Bi-LSTM的miRNA-lncRNA互作关系预测模型. 计算机研究与发展, 2019, 56(8):1652-1660.
28.孟军, 张晶, 姜丁菱, 何馨宇, 李丽双. 结合近邻传播聚类的选择性集成分类方法, 计算机研究与发展, 2018, 55(5) :986-993.
29.常征, 孟军, 施云生, 莫冯然. 多特征融合的lncRNA识别与其功能预测, 智能系统学报, 2018, 13(6):928-934.
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